KMS Chongqing Institute of Green and Intelligent Technology, CAS
Finger Gesture Recognition Using Sensing and Classification of Surface Electromyography Signals With High-Precision Wireless Surface Electromyography Sensors | |
Fu, Jianting1; Cao, Shizhou2; Cai, Linqin2; Yang, Lechan3 | |
2021-11-11 | |
摘要 | Finger gesture recognition (FGR) plays a crucial role in achieving, for example, artificial limb control and human-computer interaction. Currently, the most common methods of FGR are visual-based, voice-based, and surface electromyography (EMG)-based ones. Among them, surface EMG-based FGR is very popular and successful because surface EMG is a cumulative bioelectric signal from the surface of the skin that can accurately and intuitively represent the force of the fingers. However, existing surface EMG-based methods still cannot fully satisfy the required recognition accuracy for artificial limb control as the lack of high-precision sensor and high-accurate recognition model. To address this issue, this study proposes a novel FGR model that consists of sensing and classification of surface EMG signals (SC-FGR). In the proposed SC-FGR model, wireless sensors with high-precision surface EMG are first developed for acquiring multichannel surface EMG signals from the forearm. Its resolution is 16 Bits, the sampling rate is 2 kHz, the common-mode rejection ratio (CMRR) is less than 70 dB, and the short-circuit noise (SCN) is less than 1.5 mu V. In addition, a convolution neural network (CNN)-based classification algorithm is proposed to achieve FGR based on acquired surface EMG signals. The CNN is trained on a spectrum map transformed from the time-domain surface EMG by continuous wavelet transform (CWT). To evaluate the proposed SC-FGR model, we compared it with seven state-of-the-art models. The experimental results demonstrate that SC-FGR achieves 97.5% recognition accuracy on eight kinds of finger gestures with five subjects, which is much higher than that of comparable models. |
关键词 | surface EMG EMG sensor finger gesture recognition convolution neural network artificial limb |
DOI | 10.3389/fncom.2021.770692 |
发表期刊 | FRONTIERS IN COMPUTATIONAL NEUROSCIENCE |
卷号 | 15页码:11 |
通讯作者 | Yang, Lechan(yanglc@jit.edu.cn) |
收录类别 | SCI |
WOS记录号 | WOS:000733731400001 |
语种 | 英语 |